C4.5: programs for machine learning
C4.5: programs for machine learning
Automatic image annotation using adaptive color classification
Graphical Models and Image Processing
Pattern Recognition Letters
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
Color and Scale: The Spatial Structure of Color Images
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Segmentation and Tracking of Faces in Color Images
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Learning-Based Approach to Real Time Tracking and Analysis of Faces
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Comparison of Five Color Models in Skin Pixel Classification
RATFG-RTS '99 Proceedings of the International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems
Active Face and Feature Tracking
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Skin Detection in Video under Changing Illumination Conditions
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Data Mining
A survey of skin-color modeling and detection methods
Pattern Recognition
An adaptive multiple model approach for fast content-based skin detection in on-line videos
AREA '08 Proceedings of the 1st ACM workshop on Analysis and retrieval of events/actions and workflows in video streams
Detection of multiple people by a mobile robot in dynamic indoor environments
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
Information Security Tech. Report
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We present a comprehensive and systematic approach for skin detection. We have evaluated each component of several colour models, and then we selected a suitable colour model for skin detection. Such approach is well-known in the machine learning community as attribute selection. After listing the top components, we exemplify that a mixure of colour components can discriminate very well skin in both indoor and outdoor scenes. The spawning space created by such componens is nearly convex, therefore it allow us to use even simple rules to discriminate skin to non-skin points. These simple rules can recognise 96% of skin points with just 11% of false positives. This is a data analysis approach that will help to many skin detection systems.